Mean Surface Errors Research Articles

Digital template-guided genioplasty for patients with jaw deformity resulting from temporomandibular joint ankylosis: A comparison between single- and double-layer genioplasty

The aim of this study was to compare single- and double-layer digital template-assisted genioplasty for the correction of jaw deformity resulting from temporomandibular joint ankylosis (TMJA). Thirteen patients with jaw deformity resulting from TMJA who underwent lateral arthroplasty, costochondral graft, or total joint replacement combined with single- or double-layer digital template-assisted genioplasty were included. Computed tomography data were obtained for the preoperative design. Digital templates were designed and manufactured using three-dimensional printing to assist with the chin osteotomy and repositioning in single- or double-layer genioplasty. Of the 13 patients included, seven underwent single-layer genioplasty and six underwent double-layer genioplasty. The digital templates precisely reflected the osteotomy planes and repositioning of the chin segments intraoperatively. The radiographic evaluation showed that the patients who underwent double-layer genioplasty exhibited more chin advancement (11.95 ± 0.92 mm vs 7.50 ± 0.89 mm; P < 0.001) with a slightly larger mean surface error (1.19 ± 0.14 mm vs 0.75 ± 0.15 mm; P < 0.001) than those who underwent single-layer genioplasty. This indicates that double-layer genioplasty better promoted chin advancement and improved the facial shape, but was accompanied by more surgical error compared with the preoperative design. Furthermore, hardly any nerve damage was observed. Digital templates are useful for assisting in surgical procedures.

Compact breast shape acquisition system for improving diffuse optical tomography image reconstructions.

Diffuse optical tomography (DOT) has been investigated for diagnosing malignant breast lesions, but its accuracy relies on model-based image reconstructions, which in turn depends on the accuracy of breast shape acquisition. In this work, we have developed a dual-camera structured light imaging (SLI) breast shape acquisition system tailored for a mammography-like compression setting. Illumination pattern intensity is dynamically adjusted to account for skin tone differences, while thickness-informed pattern masking reduces artifacts due to specular reflections. This compact system is affixed to a rigid mount that can be installed into existing mammography or parallel-plate DOT systems without the need for camera-projector re-calibration. Our SLI system produces sub-millimeter resolution with a mean surface error of 0.26 mm. This breast shape acquisition system results in more accurate surface recovery, with an average 1.6-fold reduction in surface estimation errors over a reference method via contour extrusion. Such improvement translates to 25% to 50% reduction in mean squared error in the recovered absorption coefficient for a series of simulated tumors 1-2 cm below the skin.

Tree Reconstruction Using Topology Optimisation

Generating accurate digital tree models from scanned environments is invaluable for forestry, agriculture, and other outdoor industries in tasks such as identifying fall hazards, estimating trees’ biomass and calculating traversability. Existing methods for tree reconstruction rely on sparse feature identification to segment a forest into individual trees and generate a branch structure graph, limiting their application to easily separable trees and uniform forests. However, the natural world is a messy place in which trees present with significant heterogeneity and are frequently encroached upon by the surrounding environment. We present a general method for extracting the branch structure of trees from point cloud data, which estimates the structure of trees by adapting the methods of structural topology optimisation to find the optimal material distribution to interpolate the input data. We present the results of this optimisation over a wide variety of scans, and discuss the benefits and drawbacks of this novel approach to tree structure reconstruction. Our method generates detailed and accurate tree structures, with a mean Surface Error (SE) of 15 cm over 13 diverse tree datasets.

Reconstruction of three-dimensional lumbar vertebrae from biplanar x-rays

Objective. Vertebrae models from computer tomographic (CT) imaging are extensively used in image-guided surgical systems to deliver percutaneous orthopaedic operations with minimum risks, but patients may be exposed to excess radiation from the pre-operative CT scans. Generating vertebrae models from intra-operative x-rays for image-guided systems can reduce radiation exposure to the patient, and the surgeons can acquire the vertebrae’s relative positions during the operation; therefore, we proposed a lumbar vertebrae reconstruction method from biplanar x-rays. Approach. Non-stereo-corresponding vertebral landmarks on x-rays were identified as targets for deforming a set of template vertebrae; the deformation was formulated as a minimisation problem, and was solved using the augmented Lagrangian method. Mean surface errors between the models reconstructed using the proposed method and CT scans were measured to evaluate the reconstruction accuracy. Main results. The evaluation yielded mean errors of 1.27 mm and 1.50 mm in in vitro experiments on normal vertebrae and pathological vertebrae, respectively; the outcomes were comparable to other template-based methods. Significance. The proposed method is a viable alternative to provide digital lumbar to be used in image-guided systems, where the models can be used as a visual reference in surgical planning and image-guided applications in operations where the reconstruction error is within the allowable surgical error.

Generation of human thalamus atlases from 7 T data and application to intrathalamic nuclei segmentation in clinical 3 T T1-weighted images

The thalamus serves as the central relay station for the brain. It processes and relays sensory and motor signals between different subcortical regions and the cerebral cortex and it can be divided into several neuronal clusters referred to as nuclei. Each of these can possibly be subdivided into sub-nuclei. Accurate and reliable identification of thalamic nuclei is important for surgical interventions and neuroanatomical studies. This is however a challenging task because the small size of the nuclei and the lack of contrast over the thalamus region in clinically acquired images does not permit the visualization of their boundaries. A number of methods have been developed for thalamus parcellation but the vast majority of these relies on diffusion imaging or functional imaging. The low resolution of these images only permit localizing the largest nuclei. In this work we propose a method to segment smaller nuclei. We first present a protocol to build histological-like atlases from a series of high-field (7 Tesla) MR images acquired with different pulse sequences that each permits to visualize the boundaries of a subset of the nuclei. We use this protocol to scan 9 subjects and we manually delineate 23 thalamic nuclei following the Morel atlas naming convention for each of these subjects. Manual contours for the nuclei are subsequently utilized to create statistical shape models. With these data, we compare four methods for the segmentation of thalamic nuclei in 3 T images we have also acquired for the 9 subjects included in the study: (1) single atlas, (2) multi atlas, (3) statistical shape, and (4) hierarchical statistical shape in which thalamic nuclei are hierarchically fitted to the images, starting from the largest ones. Results of a leave-one-out validation study conducted on the nine image sets we have acquired show that the multi atlas approach improves upon the single atlas approach for most nuclei. Segmentations obtained with the hierarchical statistical shape model yield the highest accuracy, with dice coefficients ranging from 0.53 to 0.90, mean surface errors from 0.27 mm to 0.64 mm, and maximum surface errors from 1.31 mm to 2.52 mm for all nuclei averaged across test cases. This suggests the feasibility of using such approach for localizing thalamic substructures in clinically acquired MR volumes. It may have a direct impact on surgeries such as Deep Brain Stimulation procedures that require the implantation of stimulating electrodes in specific thalamic nuclei.

Fast and robust adaptation of organs-at-risk delineations from planning scans to match daily anatomy in pre-treatment scans for online-adaptive radiotherapy of abdominal tumors

PurposeTo validate a novel deformable image registration (DIR) method for online adaptation of planning organ-at-risk (OAR) delineations to match daily anatomy during hypo-fractionated RT of abdominal tumors. Materials and methodsFor 20 liver cancer patients, planning OAR delineations were adapted to daily anatomy using the DIR on corresponding repeat CTs. The DIR’s accuracy was evaluated for the entire cohort by comparing adapted and expert-drawn OAR delineations using geometric (Dice Similarity Coefficient (DSC), Modified Hausdorff Distance (MHD) and Mean Surface Error (MSE)) and dosimetric (Dmax and Dmean) measures. ResultsFor all OARs, DIR achieved average DSC, MHD and MSE of 86%, 2.1 mm, and 1.7 mm, respectively, within 20 s for each repeat CT. Compared to the baseline (translations), the average improvements ranged from 2% (in heart) to 24% (in spinal cord) in DSC, and 25% (in heart) to 44% (in right kidney) in MHD and MSE. Furthermore, differences in dose statistics (Dmax, Dmean and D2%) using delineations from an expert and the proposed DIR were found to be statistically insignificant (p > 0.01). ConclusionThe validated DIR showed potential for online-adaptive radiotherapy of abdominal tumors as it achieved considerably high geometric and dosimetric correspondences with the expert-drawn OAR delineations, albeit in a fraction of time required by experts.

Texture analysis improves level set segmentation of the anterior abdominal wall

The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of these hernias is fraught with failure; recurrence rates ranging from 24% to 43% have been reported, even with the use of biocompatible mesh. Currently, computed tomography (CT) is used to guide intervention through expert, but qualitative, clinical judgments, notably, quantitative metrics based on image-processing are not used. The authors propose that image segmentation methods to capture the three-dimensional structure of the abdominal wall and its abnormalities will provide a foundation on which to measure geometric properties of hernias and surrounding tissues and, therefore, to optimize intervention. In this study with 20 clinically acquired CT scans on postoperative patients, the authors demonstrated a novel approach to geometric classification of the abdominal. The authors' approach uses a texture analysis based on Gabor filters to extract feature vectors and follows a fuzzy c-means clustering method to estimate voxelwise probability memberships for eight clusters. The memberships estimated from the texture analysis are helpful to identify anatomical structures with inhomogeneous intensities. The membership was used to guide the level set evolution, as well as to derive an initial start close to the abdominal wall. Segmentation results on abdominal walls were both quantitatively and qualitatively validated with surface errors based on manually labeled ground truth. Using texture, mean surface errors for the outer surface of the abdominal wall were less than 2 mm, with 91% of the outer surface less than 5 mm away from the manual tracings; errors were significantly greater (2-5 mm) for methods that did not use the texture. The authors' approach establishes a baseline for characterizing the abdominal wall for improving VH care. Inherent texture patterns in CT scans are helpful to the tissue classification, and texture analysis can improve the level set segmentation around the abdominal region.

Multifeature Landmark-Free Active Appearance Models: Application to Prostate MRI Segmentation

Active shape models (ASMs) and active appearance models (AAMs) are popular approaches for medical image segmentation that use shape information to drive the segmentation process. Both approaches rely on image derived landmarks (specified either manually or automatically) to define the object's shape, which require accurate triangulation and alignment. An alternative approach to modeling shape is the levelset representation, defined as a set of signed distances to the object's surface. In addition, using multiple image derived attributes (IDAs) such as gradient information has previously shown to offer improved segmentation results when applied to ASMs, yet little work has been done exploring IDAs in the context of AAMs. In this work, we present a novel AAM methodology that utilizes the levelset implementation to overcome the issues relating to specifying landmarks, and locates the object of interest in a new image using a registration based scheme. Additionally, the framework allows for incorporation of multiple IDAs. Our multifeature landmark-free AAM (MFLAAM) utilizes an efficient, intuitive, and accurate algorithm for identifying those IDAs that will offer the most accurate segmentations. In this paper, we evaluate our MFLAAM scheme for the problem of prostate segmentation from T2-w MRI volumes. On a cohort of 108 studies, the levelset MFLAAM yielded a mean Dice accuracy of 88% ± 5%, and a mean surface error of 1.5 mm ±.8 mm with a segmentation time of 150/s per volume. In comparison, a state of the art AAM yielded mean Dice and surface error values of 86% ± 9% and 1.6 mm ± 1.0 mm, respectively. The differences with respect to our levelset-based MFLAAM model are statistically significant . In addition, our results were in most cases superior to several recent state of the art prostate MRI segmentation methods.

Surface scanning through a cylindrical tank of coupling fluid for clinical microwave breast imaging exams

Microwave tomographic image quality can be improved significantly with prior knowledge of the breast surface geometry. The authors have developed a novel laser scanning system capable of accurately recovering surface renderings of breast-shaped phantoms immersed within a cylindrical tank of coupling fluid which resides completely external to the tank (and the aqueous environment) and overcomes the challenges associated with the optical distortions caused by refraction from the air, tank wall, and liquid bath interfaces. The scanner utilizes two laser line generators and a small CCD camera mounted concentrically on a rotating gantry about the microwave imaging tank. Various calibration methods were considered for optimizing the accuracy of the scanner in the presence of the optical distortions including traditional ray tracing and image registration approaches. In this paper, the authors describe the construction and operation of the laser scanner, compare the efficacy of several calibration methods-including analytical ray tracing and piecewise linear, polynomial, locally weighted mean, and thin-plate-spline (TPS) image registrations-and report outcomes from preliminary phantom experiments. The results show that errors in calibrating camera angles and position prevented analytical ray tracing from achieving submillimeter accuracy in the surface renderings obtained from our scanner configuration. Conversely, calibration by image registration reliably attained mean surface errors of less than 0.5 mm depending on the geometric complexity of the object scanned. While each of the image registration approaches outperformed the ray tracing strategy, the authors found global polynomial methods produced the best compromise between average surface error and scanner robustness. The laser scanning system provides a fast and accurate method of three dimensional surface capture in the aqueous environment commonly found in microwave breast imaging. Optical distortions imposed by the imaging tank and coupling bath diminished the effectiveness of the ray tracing approach; however, calibration through image registration techniques reliably produced scans of submillimeter accuracy. Tests of the system with breast-shaped phantoms demonstrated the successful implementation of the scanner for the intended application.

Additive manufacturing for in situ repair of osteochondral defects

Tissue engineering holds great promise for injury repair and replacement of defective body parts. While a number of techniques exist for creating living biological constructs in vitro, none have been demonstrated for in situ repair. Using novel geometric feedback-based approaches and through development of appropriate printing-material combinations, we demonstrate the in situ repair of both chondral and osteochondral defects that mimic naturally occurring pathologies. A calf femur was mounted in a custom jig and held within a robocasting-based additive manufacturing (AM) system. Two defects were induced: one a cartilage-only representation of a grade IV chondral lesion and the other a two-material bone and cartilage fracture of the femoral condyle. Alginate hydrogel was used for the repair of cartilage; a novel formulation of demineralized bone matrix was used for bone repair. Repair prints for both defects had mean surface errors less than 0.1 mm. For the chondral defect, 42.8 ± 2.6% of the surface points had errors that were within a clinically acceptable error range; however, with 1 mm path planning shift, an estimated ∼75% of surface points could likely fall within the benchmark envelope. For the osteochondral defect, 83.6 ± 2.7% of surface points had errors that were within clinically acceptable limits. In addition to implications for minimally invasive AM-based clinical treatments, these proof-of-concept prints are some of the only in situ demonstrations to-date, wherein the substrate geometry was unknown a priori. The work presented herein demonstrates in situ AM, suggests potential biomedical applications and also explores in situ-specific issues, including geometric feedback, material selection and novel path planning techniques.

Flux Mapping the 400 m2 “Big Dish” at the Australian National University

Videographic flux mapping has been used to characterise the focal flux distribution of the 400 m2 solar concentrator located at the Australian National University. Peak fluxes in the focal region of 1.13 MW m−2 have been recorded, which corresponds to a concentration of 1790 suns normalized to 1000 W m−2 insolation and 100 percent mirror reflectivity. Comparisons with ray trace predicted fluxes incorporating surface normal errors indicates that the dish surface has a mean surface error standard deviation of 6 milliradian.